Data science is not a science. It is a research paradigm. Its power, scope,
and scale will surpass science, our most powerful research paradigm, to enable
knowledge discovery and change our world. We have yet to understand and define
it, vital to realizing its potential and managing its risks. Modern data
science is in its infancy. Emerging slowly since 1962 and rapidly since 2000,
it is a fundamentally new field of inquiry, one of the most active, powerful,
and rapidly evolving 21st century innovations. Due to its value, power, and
applicability, it is emerging in 40+ disciplines, hundreds of research areas,
and thousands of applications. Millions of data science publications contain
myriad definitions of data science and data science problem solving. Due to its
infancy, many definitions are independent, application-specific, mutually
incomplete, redundant, or inconsistent, hence so is data science. This research
addresses this data science multiple definitions challenge by proposing the
development of coherent, unified definition based on a data science reference
framework using a data science journal for the data science community to
achieve such a definition. This paper provides candidate definitions for
essential data science artifacts that are required to discuss such a
definition. They are based on the classical research paradigm concept
consisting of a philosophy of data science, the data science problem solving
paradigm, and the six component data science reference framework (axiology,
ontology, epistemology, methodology, methods, technology) that is a frequently
called for unifying framework with which to define, unify, and evolve data
science. It presents challenges for defining data science, solution approaches,
i.e., means for defining data science, and their requirements and benefits as
the basis of a comprehensive solution.